I wrote an explainer on classic edge detection algorithms (Sobel, Canny, Laplacian) while building an interactive tool that lets you upload an image and compare their outputs side by side.
The post focuses on:
- What each algorithm is actually doing under the hood
- Where each one works well (and where it doesn’t)
- Why Canny feels “better” in many cases but costs more compute
- Practical tradeoffs rather than just theory
The tool runs entirely client-side, so no images are uploaded to a server.
Would love feedback from folks who’ve used these algorithms in production or teaching.
Most Git tutorials assume you’re an engineer, but with the rise in vibe coding tools, PM role is changing to a more collaborative builder role. Vibe coding PMs also need to understand branches, pull requests, code reviews, and how to collaborate effectively with dev teams.
I put together a concise, no-nonsense guide that explains Git concepts using product-friendly examples and shows how PMs can use Git to vibe-code, explore repos, understand changes, and communicate better with engineers.
Would love feedback from PMs, EMs, and devs:
– What do PMs struggle with the most when learning Git?
– Does this level of depth feel right?
– What else should be added to make it truly useful in real workflows?
Open to critique, improvements, and additional topic suggestions.
Many teams end up gluing their app to a background job queue through n8n webhooks, and then discover all the sharp edges: duplicate callbacks, mismatched job states, retries that corrupt data, and workflows that quietly stall under load.
This post walks through a pattern for wiring a custom job queue into n8n using webhook callbacks, with a focus on idempotency, explicit job state transitions, and failure recovery rather than “happy path” demos.
Curious how others are handling this in production:
- How do you design your callback contracts and job state machine?
- What patterns have worked (or failed) for idempotent callbacks and retries?
- Any war stories from running webhook-heavy n8n (or similar) setups at scale?
I've seen too many comments on how production automation workflows fail silently or bring
down entire systems when a single API call times out.
Just published a guide covering the error handling, retry logic, and
security patterns that prevent this:
Some problems:
- Single unhandled error cascades through your entire workflow
- No visibility into what failed or why
- No automatic recovery from transient failures
- Webhooks are wide-open security holes
General Solutions:
- Centralized error workflows that capture full failure context
- Exponential backoff retry logic
- Try/Catch blocks for risky operations
- Webhook authentication with HMAC validation
Also included: An interactive retry logic simulator, webhook security
tester, and a complete production deployment checklist.
Would love feedback from HN on what else should be covered for production
n8n deployments.
This we app lets you format/edit/minify multiple json at the same time. With focus on various small productivity tricks like -
1. Copy a child object in a click
2. Copy minified string in a click
3. Remove common problems
4. Give a filename and download edited/formatted JSON in a click
It might (or might not) be worth making a short "screencast" style animation showing these things if making one is easy enough. I say this because my experience felt like landing in the middle of a random editor...the page didn't provide any context...so maybe a minimum landing page listing these features is a more reasonable alternative to an animation. Yes, a landing page probably is a better alternative to an animation. And to me, it's a better alternative to dropping first time visitors into the editor. Good luck.
Thanks for the feedback!
Yes, I have been thinking of creating a "How do I" or "Help" page. I have created small videos/GIFs of few features. Will embed them on the website.
I have been working with large JSON objects quite a lot in recent past. One of the main problems I see with every online JSON formatter out there is that if I need to work with multiple JSON then I have to open multiple tabs and there was no way to keep track of where what is.
I created jsontoolbox to scratch my own itch and may be help others who might have similar problems.
Here are some features ->
1. Format/Minify multiple JSON on the same page.
2. Copy a sub object/value in a single click.
3. Copy minified string in a single click on formatter.
4. Name every JSON and quickly download with same file name.
Hope this is useful for some one else as well. Looking for feedback.
The post focuses on:
- What each algorithm is actually doing under the hood
- Where each one works well (and where it doesn’t)
- Why Canny feels “better” in many cases but costs more compute
- Practical tradeoffs rather than just theory
The tool runs entirely client-side, so no images are uploaded to a server.
Would love feedback from folks who’ve used these algorithms in production or teaching.